Cancer is one of the most deadly threats to human health. In the U.S. alone, cancer affects nearly 1.3 million new patients each year, and is the second leading cause of death after cardiovascular disease, accounting for approximately 1 in 4 deaths. Solid tumors are responsible for most of those deaths. Although there have been significant advances in the medical treatment of certain cancers, the overall 5-year survival rate for all cancers has improved only by about 10% in the past 20 years. Cancers, or malignant tumors, metastasize and grow rapidly in an uncontrolled manner, making timely detection and treatment extremely difficult.
Depending on the cancer type, patients typically have several treatment options available to them including chemotherapy, radiation and antibody-based drugs. Patients frequently develop resistance to one or more cancer treatments. Frequently this resistance is associated with a mutation in the tumor. There currently are no methods available to predict or monitor patients for the development of resistance to cancer treatments.
Complicating the treatment of cancer is the long timeline for the development of new chemotherapeutic agents. The current methodology of small molecule drug discovery is risky due to the lone and expensive development and clinical trial process that occurs prior to validation of the drug in patients. Additionally, the attrition rate for these drugs is high because determination of the drug candidate's efficacy occurs late in the development process after massive expenditures have already occurred. The accumulated costs of the 4-6 years of pre-clinical and Phase 1 clinical trials are large and highly risky for the drug owner.
Thus, there is a need for more effective means for determining which patients will respond to specific cancer therapeutics, to predict which patients will develop resistance to cancer therapeutics and for incorporating such determinations into more effective treatment regimens for patients with anti-cancer therapies. Additionally, there is a need for better methods of quickly predicting which small molecules will be clinically beneficial prior to the need for expensive clinical trials.
Described herein is the use of a proprietary crystal structure library and a unique pattern matching algorithm to predict the functionality of a gene mutation, predict the specificity of a small molecule kinase inhibitor and to streamline drug development by the prediction of virtual molecules to inhibit kinases, for example by identifying previously unknown intermediate states of kinase catalytic cores resulting from activating cancer mutations. This predictive algorithm has been used to select appropriate therapeutic agents to target specific mutations as well as predict or monitor the development of resistance to therapeutic agents based in specific mutations. Further, the predictive algorithm methodology enables the rapid design of new drug candidates based on the specificity profile for the predicted functionality of a mutation.